Mapping Ligand Binding Landscapes using Weighted Ensembles of Trajectories

Abstract

Protein-ligand interactions involve many conformational states. To bind the ligand must search across a rough free energy landscape that contains many metastable minima. A map of this “ligand binding landscape” would be useful during drug design, as it contains a set of possible binding modes that could be stabilized by modifications of the ligand. These maps would also include information about pose connectivity, both with each other and with the unbound state, which can provide insight into ligand binding kinetics, a useful parameter for drug design. Unfortunately, creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The Dickson lab has developed simulation algorithms (e.g. WExplore, REVO) that can broadly and efficiently explore free-energy landscapes and are capable of simulating ligand release pathways that occur on timescales as long as minutes. Importantly, these methods employ only unbiased trajectory segments, allowing for the construction of Markov state models (MSM) and conformation space networks that combine the results of multiple simulations. In this talk I will describe how these methods have revealed new insights into long timescale ligand release pathways, as well as the interconversion pathways between ligand poses. In particular I will focus on the application of WExplore to map the binding landscapes of two bromodomain-inhibitor systems (Brd4-MS436 and Baz2B-ICR7), and the use of Markov state models to predict stable poses. In this work we find that transitions between deeply bound states convert through the unbound state 81% of the time, implying a trial-and-error approach to ligand binding. This finding has consequences for both the way we think about ligand binding processes, and how molecular dynamics should be used in drug design.